大规模网络效用最大化机制设计

Meng Zhang, Deepanshu Vasal
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引用次数: 0

摘要

网络效用最大化(NUM)是设计分布式网络优化算法的通用框架。现有的研究提出了(经济)机制来解决NUM,但在很大程度上忽视了大规模实施的问题。在本文中,我们提出了具有更简单支付规则的NUM的大规模Vickery-Clark-Grove (VCG)机制。大规模VCG机制使网络效用最大化,实现个体理性和预算平衡。我们表明,当智能体的数量接近无穷大时,每个智能体的误报动机会二次收敛到零。对于实际实现,我们引入了一种修改后的机制,该机制具有额外的重要技术属性,即可叠加性,这使得它能够构建在任何(潜在的分布式)算法上,这些算法可以最佳地解决NUM问题,并确保代理遵守算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mechanism Design for Large Scale Network Utility Maximization
Network utility maximization (NUM) is a general framework for designing distributed optimization algorithms for networks. Existing studies proposed (economic) mechanisms to solve the NUM but largely neglected the issue of large-scale implementation. In this paper, we present the Large-Scale Vickery-Clark-Grove (VCG) Mechanism for NUM with a simpler payment rule. The Large-Scale VCG Mechanism maximizes the network utility and achieves individual rationality and budget balance. We show that, as the number of agents approaches infinity, each agent's incentive to misreport converges quadratically to zero. For practical implementation, we introduce a modified mechanism that possesses an additional important technical property, superimposability, which makes it able to be built upon any (potentially distributed) algorithm that optimally solves the NUM Problem and ensures agents to obey the algorithm.
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